1. Field of the Invention
This invention pertains to the enhancement of objects in radar signal processing. More specifically, this invention pertains to using the Wavelet Transform in combination with fractal geometry techniques to estimate the fractal dimension of radar signatures in the presence of high background noise.
2. Description of the Related Art
Detecting stationary targets in a ground clutter background is a difficult problem for airborne radar sensors. Traditionally, the employed radar signal processing technique is adaptive amplitude thresholding such as constant false alarm rate (CFAR) processing that exploits the target's radar cross section for detecting the target. However, when using radar amplitude processing techniques, achieving the desired high probabilities of detection only occurs by setting the thresholds very low, which often results in an unacceptable number of false-alarm targets due to background clutter. Increasing the detection thresholds to produce a more acceptable number of false alarms unfortunately gives us an unacceptably low detection probability. Overcoming the above difficulties lead to the development of other radar signal processing techniques using different target attributes. These other techniques primarily related to the target's geometry that include polarization and other high resolution techniques.
Fractal geometry techniques use a numerical value of the fractal dimensions of radar signatures to distinguish man-made or artificial (also known as "regular") objects from "natural" objects that produce background clutter or noise. This invention uses the relationship between a newly developed mathematical transform, known as the Wavelet Transform, and fractal geometry to produce a robust, computationally efficient method to measure or estimate the fractal dimension of radar signatures, even in the presence of significant noise, reflected from the surface of a target. Current radar processors can incorporate this invention to increase the systems' ability to distinguish targets from background clutter.
The multiple-scale resolution capabilities of the Wavelet Transform make wavelet coefficients a natural set of coordinates in which to examine scale-invariant fractal signals such as radar returns from natural surfaces. The use of wavelet coefficients by this invention forms the basis of useful categorization algorithms leading to the capability of distinguishing manmade from natural reflectors by using the object's roughness. Combining wavelet coefficients with neural networks provide robust estimates of fractal dimension even in the presence of significant noise. Such a combination involves relative simplicity of implementation while affording great flexibility in accommodating a broad range of signal types and noise levels.